Optimal Kinematics Design of MacPherson Suspension: Integrated Use of Grey Relational Analysis and Improved Entropy Weight Method

Selecting design variables and determining optimal hard⁃point coordinates are subjective in the traditional multiobjective optimization of geometric design of vehicle suspension,thereby usually resulting in poor overall suspension kinematic performance.To eliminate the subjectivity of selection,a method transferring multiobjective optimization function into a single⁃objective one through the integrated use of grey relational analysis(GRA)and improved entropy weight method(IEWM)is proposed.First,a comprehensive evaluation index of sensitivities was formulated to facilitate the objective selection of design variables by using GRA,in which IEWM was used to determine the weight of each subindex.Second,approximate models between the variations of the front wheel alignment parameters and the design variables were developed on the basis of support vector regression(SVR)and the fruit fly optimization algorithm(FOA).Subsequently,to eliminate the subjectivity and improve the computational efficiency of multiobjective optimization(MOO)of hard⁃point coordinates,the MOO functions were transformed into a single⁃objective optimization(SOO)function by using the GRA-IEWM method again.Finally,the SOO problem was solved by the self⁃adaptive differential evolution(jDE)algorithm.Simulation results indicate that the GRA⁃IEWM method outperforms the traditional multiobjective optimization method and the original coordinate scheme remarkably in terms of kinematic performance.

Comparisons on methods for identifying accident black spots using vehicle kinetic parameters collected from road experiments

Identification of accident black spots has gained tremendous popularity among road agencies and safety specialists for evaluating and subsequently enhancing road traffic safety.However,there is still limited understanding of the internal relationship between black spots and microscopic vehicle kinetic parameters.To address this gap,this paper describes a project that was undertaken using the real-time tire force data(kinetic response)obtained from road experiments on Wenli Expressway.First,factor analysis was applied to extracted three independent indicators(power-braking,handling stability,and ride comfort)from seven original kinetic indicators with multiple collinearities.Afterward,the main indicators were given vehicle kinetic meaning by analyzing the characteristics of original indicators associated with them.A compelling correlation was established among kinetic parameters,vehicle running qualities,and accident risk.Additionally,an integrated evaluation framework was established to identify accident black spots based on applying ordered logit models and PLS-entropy-TOPSIS approaches.The recognition results exhibited that the overall recognition accuracy obtained by the latter was found to be comparable to that achieved using the previous one.The compound evaluation model proposed in this paper has been proven to present many advantages for black spot identification.It is evidently clear from the findings that the vehicle kinetic parameters have significant correlations with road accident risk.This paper could provide some insightful knowledge for identifying and preventing the black spots from ameliorating traffic safety.